Biological noise abatement: coordinating the responses of autonomous bacteria in a synthetic biofilm to a fluctuating environment using a stochastic bistable switch

Engineered Control of Genetic Variability Reveals Interplay among Quorum Sensing, Feedback Regulation, and Biochemical Noise

Stochastic fluctuations in gene expression trigger both beneficial and harmful consequences for cell behavior. Therefore, achieving a desired mean protein expression level while minimizing noise is of interest in many applications, including robust protein production systems in industrial biotechnology. Here, we consider a synthetic gene circuit combining intracellular negative feedback and cell-to-cell communication based on quorum sensing. Accounting for both intrinsic and extrinsic noise, stochastic simulations allow us to analyze the capability of the circuit to reduce noise strength as a function of its parameters. We obtain mean expression levels and noise strengths for all species under different scenarios, showing good agreement with system-wide available experimental data of protein abundance and noise in Escherichia coli. Our in silico experiments, validated by preliminary in vivo results, reveal significant noise attenuation in gene expression through the interplay between quorum sensing and negative feedback and highlight the differential role that they play in regard to intrinsic and extrinsic noise.

Taking control over microbial populations: Current approaches for exploiting biological noise in bioprocesses

Phenotypic plasticity of microbial cells has attracted much attention and several research efforts have been dedicated to the description of methods aiming at characterizing phenotypic heterogeneity and its impact on microbial populations. However, different approaches have also been suggested in order to take benefit from noise in a bioprocess perspective, e.g. by increasing the robustness or productivity of a microbial population. This review is dedicated to outline these controlling methods. A common issue, that has still to be addressed, is the experimental identification and the mathematical expression of noise. Indeed, the effective interfacing of microbial physiology with external parameters that can be used for controlling physiology depends on the acquisition of reliable signals. Latest technologies, like single cell microfluidics and advanced flow cytometric approaches, enable linking physiology, noise, heterogeneity in productive microbes with environmental cues and hence allow correctly mapping and predicting biological behavior via mathematical representations. However, like in the field of electronics, signals are perpetually subjected to noise. If appropriately interpreted, this noise can give an additional insight into the behavior of the individual cells within a microbial population of interest. This review focuses on recent progress made at describing, treating and exploiting biological noise in the context of microbial populations used in various bioprocess applications.

Wiring Together Synthetic Bacterial Consortia to Create a Biological Integrated Circuit

The promise of adapting biology to information processing will not be realized until engineered gene circuits, operating in different cell populations, can be wired together to express a predictable function. Here, elementary biological integrated circuits (BICs), consisting of two sets of transmitter and receiver gene circuit modules with embedded memory placed in separate cell populations, were meticulously assembled using live cell lithography and wired together by the mass transport of quorum-sensing (QS) signal molecules to form two isolated communication links (comlinks). The comlink dynamics were tested by broadcasting "clock" pulses of inducers into the networks and measuring the responses of functionally linked fluorescent reporters, and then modeled through simulations that realistically captured the protein production and molecular transport. These results show that the comlinks were isolated and each mimicked aspects of the synchronous, sequential networks used in digital computing. The observations about the flow conditions, derived from numerical simulations, and the biofilm architectures that foster or silence cell-to-cell communications have implications for everything from decontamination of drinking water to bacterial virulence.